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Development of aboveground mangrove forests’ biomass dataset for Southeast Asia based on ALOS-PALSAR 25-m mosaic

Darmawan S.a, Sari D.K.a, Takeuchi W., Wikantika K.c, Hernawati R.a

a Institut Teknologi Nasional Bandung, Department of Geodetic Engineering, Bandung, Indonesia
b University of Tokyo, Institute of Industrial Science, Tokyo, Japan
c InstitutTeknologi Bandung, Center for Remote Sensing, Bandung, Indonesia

[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.Southeast Asia (SEA) has the largest mangrove forest area in the world, which plays an important role in the global carbon cycle and is helping to mitigate climate change. In order to manage the mangrove forests in SEA, their total biomass needs to be determined. However, development of a biomass dataset based on field survey is time consuming. An aboveground biomass (AGB) dataset of mangrove forests was developed for SEA based on ALOS PALSAR 25-m mosaic. Specifically, ALOS-PALSAR 25-m images were first retrieved for SEA from the Kyoto and Carbon Initiative projects and then converted from a digital number to a normalized radar cross-section format in decibels. Samples of mangrove forests in SEA were collected as regions of interest from ALOS PALSAR data based on visual interpretation using Landsat data and Google Earth imagery. A rule-based classification method based on mangrove backscattering characteristics was then used to classify mangroves and nonmangroves in the region. Subsequently, an empirical model was adopted to estimate the AGB of the mangrove forests and an AGB dataset was developed. The results indicate that the spatial distribution of mangrove forests over SEA is 5.1 million hectares, and the estimated average AGB is 140.5 ± 136.1 Mg / ha.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Above ground biomass,ALOS PALSAR,Mangrove forest,Normalized radar cross section,Regions of interest,Rule-based classification,Southeast Asia,Visual interpretation[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]aboveground biomass,ALOS PALSAR,backscattering,mangrove forests,Southeast Asia[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]The authors would like to thank the Ministry of Research, Technology, and Higher Education of the Republic of Indonesia (RISTEKDIKTI) for all their support. Also the authors would like to thank LPPM-ITENAS, JAXA-JAPAN, and Anggun Tridawati from Lampung University for their support.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1117/1.JRS.13.044519[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]